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Liver test abnormalities are frequently observed in patients with coronavirus disease 2019 (COVID‐19) and are associated with worse prognosis. However, information is limited about pathological changes in the liver in this infection, so the mechanism of liver injury is unclear. Here we describe liver histopathology and clinical correlates of 27 patients who died of COVID‐19 in Manaus, Brazil. There was a high prevalence of liver injury (elevated alanine aminotransferase and aspartate aminotransferase in 44% and 48% of patients, respectively) in these patients. Histological analysis showed sinusoidal congestion and ischemic necrosis in more than 85% of the cases, but these appeared to be secondary to systemic rather than intrahepatic thrombotic events, as only 14% and 22% of samples were positive for CD61 (marker of platelet activation) and C4d (activated complement factor), respectively. Furthermore, the extent of these vascular findings did not correlate with the extent of transaminase elevations. Steatosis was present in 63% of patients, and portal inflammation was present in 52%. In most cases, hepatocytes expressed angiotensin‐converting enzyme 2 (ACE2), which is responsible for binding and entry of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), even though this ectoenzyme was minimally expressed on hepatocytes in normal controls. However, SARS‐CoV‐2 staining was not observed. Most hepatocytes also expressed inositol 1,4,5‐triphosphate receptor 3 (ITPR3), a calcium channel that becomes expressed in acute liver injury. Conclusion: The hepatocellular injury that commonly occurs in patients with severe COVID‐19 is not due to the vascular events that contribute to pulmonary or cardiac damage. However, new expression of ACE2 and ITPR3 with concomitant inflammation and steatosis suggests that liver injury may result from inflammation, metabolic abnormalities, and perhaps direct viral injury. [ABSTRACT FROM AUTHOR]

To investigate the influence of climate on hospitalizations of sickle cell anemia (SCA) adults and children, we analyzed the health and meteorological parameters from a metropolis (1999-2018). 1462 hospitalizations were coded for SCA patients in crisis (M:F = 715:747) and 1354 hospitalizations for SCA patients without crisis (M:F = 698:656) [age = 22.9 vs 15.2 years and duration of hospitalization (DoH) = 5.7 vs 4.4 days, respectively,]. More hospitalizations were for adults than children in crisis, and for children than adults without crisis. More children and adults were hospitalized in winter andspring than in summer and autumn     Hospitalizations correlated positively with humidity (lag -5), maximum pressure (lag -2), mean pressure (lag -2), and thermal amplitude (lag -2), and negatively with maximum temperature (lag -3). DoH positively correlated with minimum temperature (lag -4). Understanding these complex associations would induce attitudinal/behavioral modifications among patients and their caregivers.

The outbreak of SARS-CoV-2 in Wuhan, China in late December 2019 became the harbinger of the COVID-19 pandemic. During the pandemic, geospatial techniques, such as modeling and mapping, have helped in disease pattern detection. Here we provide a synthesis of the techniques and associated findings in relation to COVID-19 and its geographic, environmental, and socio-demographic characteristics, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology for scoping reviews. We searched PubMed for relevant articles and discussed the results separately for three categories: disease mapping, exposure mapping, and spatial epidemiological modeling. The majority of studies were ecological in nature and primarily carried out in China, Brazil, and the USA. The most common spatial methods used were clustering, hotspot analysis, space-time scan statistic, and regression modeling. Researchers used a wide range of spatial and statistical software to apply spatial analysis for the purpose of disease mapping, exposure mapping, and epidemiological modeling. Factors limiting the use of these spatial techniques were the unavailability and bias of COVID-19 data-along with scarcity of fine-scaled demographic, environmental, and socio-economic data-which restrained most of the researchers from exploring causal relationships of potential influencing factors of COVID-19. Our review identified geospatial analysis in COVID-19 research and highlighted current trends and research gaps. Since most of the studies found centered on Asia and the Americas, there is a need for more comparable spatial studies using geographically fine-scaled data in other areas of the world.

Purpose: Reliable predictors of HIV disease progression are scarce in developing countries, where most HIV infections occur. We describe early virologic and immunologic events among men who have sex with men in Rio de Janeiro, Brazil., Methods: Seroconverters from 2 high-risk cohorts were followed for up to 36 months with periodic laboratory evaluations, plasma viral load, and CD4 count assessments. Viral load and CD4 count mean trajectories were computed. For the modeled viral loads, mean and median values were 24,480 (4.36 log10) and 19,720 (4.29 log10) copies/mL (range 14,880-58,090), respectively. Median CD4 count was 373 cells/microL (range 260-508). Overall variation on viral loads ranged from 4.3 to 5.2 log10 copies/mL with a visible increase in the viral load starting at approximately 600 days (n = 12) after estimated time of seroconversion. The initial period of HIV infection was characterized by an increase in CD4 count (n = 29) followed by a steep decline starting at approximately 200 days (508 cells, 95% CI: 425 to 569). A gradual decrease was observed in the median CD4 count thereafter, reaching 281 (95% CI: 100 to 466) at 1000 days after the estimated date of seroconversion., Conclusions: Although viral load dynamics resembled those observed in developed countries, CD4 counts seem to decline at a faster rate than in the Multicenter AIDS Cohort Study (MACS) cohort. Clinical and survival data are needed to assess the impact of interventions, such as antiretroviral therapy, on the clinical course of HIV infection in Brazil.